A Technicoeconomic Approach for the Selection of a Site Remediation Strategy—Part A: Theory

A technicoeconomic model is developed to select an optimal strategy for the remediation of a contaminated site and to determine the value of this remediation strategy. The model is an extension of actual cost–benefit analysis, with consideration of “irreversible” remediation technology choices, technology effectiveness, and uncertainty on the site's level of contamination. The model considers the possibility of reducing uncertainty by both acquiring more and better information on the level of contamination and by offering the decision-maker the opportunity to reevaluate his decision and switch to a more appropriate technology. It is believed that this model will help decision-makers in the selection of a remediation strategy by presenting all potentially feasible strategies, and how uncertainty on the site's level of contamination affects these strategies.     

A Proactive Approach Can Make Site Remediation Less Expensive

Without any incentive to clean up a contaminated site, remediation is often delayed until the site owner is compelled to act by regulatory agencies. In such a context, the selected technology is typically the one that will reach the remediation goals as quickly as possible. Unfortunately, this criterion is often met by overly expensive technologies, resulting in high and sometimes unaffordable total remediation costs, leading to a remediation with a negative net benefit. This study examines the effects of time constraint and benefit value on the optimal remediation strategy for a diesel-contaminated site. This strategy is developed using the technico-economic model METEORS, which takes into account the technology’s effectiveness, the uncertainty of the level of contamination, and the possibility of reducing this uncertainty through either an additional characterization (before selecting and applying a technology) or the monitoring of the remediation technology (during its use). Results of simulations with both economic and temporal constraints support a proactive approach to site remediation.     

A technicoeconomic approach for the selection of a site remediation strategy--part B: model application

Different tools, such as a screening matrix or decision framework, are available to select a remediation technology to treat a contaminated site. However, unless these methods can point out the appropriate technology in regards to the decision-maker's knowledge about the contaminated site, they are less useful to evaluate both the technical effectiveness and the cost of the remediation, and to assess different remediation strategies from either future data acquisition or the use of an irreversible remediation technology. A model developed to allow such evaluations has been used to simulate the remediation of a virtual contaminated site. From this, four remediation recommendations have been made. These recommendations are guidelines for the build up of a remediation strategy that would both maximize the effectiveness of the decontamination and minimize its total cost.     

GROUND WATER SOLUTE TRANSPORT,OPTIMAL REMEDIATION PLANNING,AND DECISION MAKING UNDER UNCERTAINTY1

ABSTRACT: A groundwater quality modeling advisory system has been developed for the U.S. Air Force for use in investigating remediation alternatives for the cleanup of subsurface contamination. The system is capable of accounting for uncertainty, not only in the prediction of solute transport but also in the optimization of the remediation scheme through chance constraints. The system guides users in the selection of appropriate transport models through an algorithm independently tested with machine learning codes. An application to Hill Air Force Base, Utah, is presented for which different pump-and-treat strategies are considered: the results are evaluated in terms of the cumulative distribution of the contaminant concentration for each case and the tradeoff relationship between the cost of remediation and the probability that the remediation strategy exceeds an established maximum allowable contaminant concentration.     

A Technicoeconomic Approach for theSelection of a Site Remediation Strategy—Part B: Model Application

Different tools, such as a screening matrix or decision framework, are available to select a remediation technology to treat a contaminated site. However, unless these methods can point out the appropriate technology in regards to the decision-maker's knowledge about the contaminated site, they are less useful to evaluate both the technical effectiveness and the cost of the remediation, and to assess different remediation strategies from either future data acquisition or the use of an irreversible remediation technology. A model developed to allow such evaluations has been used to simulate the remediation of a virtual contaminated site. From this, four remediation recommendations have been made. These recommendations are guidelines for the build up of a remediation strategy that would both maximize the effectiveness of the decontamination and minimize its total cost.     

土壤中砷的污染控制技术研究

土壤砷污染修复是世界性的难题,日益受到人们的密切关注。本文阐述了土壤砷污染的现状、危害及其来源,探讨土壤砷污染的传统物理化学和生物修复技术的研究现状及特点的同时,重点阐述了纳米材料修复技术,尤其是纳米铁技术,并对土壤砷污染修复研究方向进行了展望。     

Forecasting the number of soil samples required to reduce remediation cost uncertainty

Sampling scheme design is an important step in the management of polluted sites. It largely controls the accuracy of remediation cost estimates. In practice, however, sampling is seldom designed to comply with a given level of remediation cost uncertainty. In this paper, we present a new technique that allows one to estimate of the number of samples that should be taken at a given stage of investigation to reach a forecasted level of accuracy. The uncertainty is expressed both in terms of volume of polluted soil and overall cost of remediation. This technique provides a flexible tool for decision makers to define the amount of investigation worth conducting from an environmental and financial perspective. The technique is based on nonlinear geostatistics (conditional simulations) to estimate the volume of soil that requires remediation and excavation and on a function allowing estimation of the total cost of remediation (including investigations). The geostatistical estimation accounts for support effect, information effect, and sampling errors. The cost calculation includes mainly investigation, excavation, remediation, and transportation. The application of the technique on a former smelting work site (lead pollution) demonstrates how the tool can be used. In this example, the forecasted volumetric uncertainty decreases rapidly for a relatively small number of samples (20-50) and then reaches a plateau (after 100 samples). The uncertainty related to the total remediation cost decreases while the expected total cost increases. Based on these forecasts, we show how a risk-prone decision maker would probably decide to take 50 additional samples while a risk-averse decision maker would take 100 samples.     

Probabilistic fugacity analysis of Lake Pontchartrain pollution after Hurricane Katrina

After Hurricane Katrina passed through the US Gulf Coast in August 2005, floodwaters covering New Orleans were pumped into Lake Pontchartrain as part of the rehabilitation process in order to make the city habitable again. The long-term consequences of this environmentally critical decision were difficult to assess at the time and were left to observation. In the aftermath of these natural disasters, and in cases of emergency, the proactive use of screening level models may prove to be an important factor in making appropriate decisions to identify cost effective and environmentally friendly mitigation solutions. In this paper, we propose such a model and demonstrate its use through the application of several hypothetical scenarios to examine the likely response of Lake Pontchartrain to the contaminant loading that were possibly in the New Orleans floodwaters. For this purpose, an unsteady-state fugacity model was developed in order to examine the environmental effects of contaminants with different physicochemical characteristics on Lake Pontchartrain. The three representative contaminants selected for this purpose are benzene, atrazine, and polychlorinated biphenyls (PCBs). The proposed approach yields continuous fugacity values for contaminants in the water, air, and sediment compartments of the lake system which are analogous to concentrations. Since contaminant data for the floodwaters are limited, an uncertainty analysis was also performed in this study. The effects of uncertainty in the model parameters were investigated through Monte Carlo analysis. Results indicate that the acceptable recovery of Lake Pontchartrain will require a long period of time. The computed time range for the levels of the three contaminants considered in this study to decrease to maximum contaminant levels (MCLs) is about 1 year to 68 years. The model can be implemented to assess the possible extent of damage inflicted by any storm event on the natural water resources of Southern Louisiana or similar environments elsewhere. Furthermore, the model developed can be used as a useful decision-making tool for planning and remediation in similar emergency situations by examining various potential contamination scenarios and their consequences.     

THE USE OF A SIMPLE MODEL AND UNCERTAINTY ANALYSIS IN LAKE MANAGEMENT1

ABSTRACT: A simple, black-box lake model was developed for phosphorus, using nonlinear regression analysis on a data base of north temperate lakes. The uncertainty associated with the model was then combined with the parameter uncertainty and the independent variable uncertainty to provide an estimate of the confidence limits associated with a predicted value. The prediction uncertainty is often neglected, yet it is an important measure of the usefulness of a model. Prediction uncertainty reflects the modeler's confidence in the model, and it should be used by a decision maker as a weight indicating the value of the model prediction. A procedure is outlined that combined lake modeling and uncertainty analysis for use in lake quality assessment and lake management. An example is provided illustrating the use of this procedure in nutrient budget sampling design, data analysis, and the evaluation of lake management strategies for a 208 program in New Hampshire.     

A free-enzyme catalyst for the bioremediation of environmental atrazine contamination

Herbicide contamination from agriculture is a major issue worldwide, and has been identified as a threat to freshwater and marine environments in the Great Barrier Reef World Heritage Area in Australia. The triazine herbicides are of particular concern because of potential adverse effects, both on photosynthetic organisms and upon vertebrate development. To date a number of bioremediation strategies have been proposed for triazine herbicides, but are unlikely to be implemented due to their reliance upon the release of genetically modified organisms. We propose an alternative strategy using a free-enzyme bioremediant, which is unconstrained by the issues surrounding the use of live organisms. Here we report an initial field trial with an enzyme-based product, demonstrating that the technology is technically capable of remediating water bodies contaminated with the most common triazine herbicide, atrazine.     

Optimal Environmental Management Strategy and Implementation for Groundwater Contamination Prevention and Restoration

An innovative management strategy is proposed for optimized and integrated environmental management for regional or national groundwater contamination prevention and restoration allied with consideration of sustainable development. This management strategy accounts for availability of limited resources, human health and ecological risks from groundwater contamination, costs for groundwater protection measures, beneficial uses and values from groundwater protection, and sustainable development. Six different categories of costs are identified with regard to groundwater prevention and restoration. In addition, different environmental impacts from groundwater contamination including human health and ecological risks are individually taken into account. System optimization principles are implemented to accomplish decision-makings on the optimal resources allocations of the available resources or budgets to different existing contaminated sites and projected contamination sites for a maximal risk reduction. Established management constraints such as budget limitations under different categories of costs are satisfied at the optimal solution. A stepwise optimization process is proposed in which the first step is to select optimally a limited number of sites where remediation or prevention measures will be taken, from all the existing contaminated and projected contamination sites, based on a total regionally or nationally available budget in a certain time frame such as 10 years. Then, several optimization steps determined year-by-year optimal distributions of the available yearly budgets for those selected sites. A hypothetical case study is presented to demonstrate a practical implementation of the management strategy. Several issues pertaining to groundwater contamination exposure and risk assessments and remediation cost evaluations are briefly discussed for adequately understanding implementations of the management strategy.     

Remediation technologies for heavy metal contaminated groundwater

The contamination of groundwater by heavy metal, originating either from natural soil sources or from anthropogenic sources is a matter of utmost concern to the public health. Remediation of contaminated groundwater is of highest priority since billions of people all over the world use it for drinking purpose. In this paper, thirty five approaches for groundwater treatment have been reviewed and classified under three large categories viz chemical, biochemical/biological/biosorption and physico-chemical treatment processes. Comparison tables have been provided at the end of each process for a better understanding of each category. Selection of a suitable technology for contamination remediation at a particular site is one of the most challenging job due to extremely complex soil chemistry and aquifer characteristics and no thumb-rule can be suggested regarding this issue. In the past decade, iron based technologies, microbial remediation, biological sulphate reduction and various adsorbents played versatile and efficient remediation roles. Keeping the sustainability issues and environmental ethics in mind, the technologies encompassing natural chemistry, bioremediation and biosorption are recommended to be adopted in appropriate cases. In many places, two or more techniques can work synergistically for better results. Processes such as chelate extraction and chemical soil washings are advisable only for recovery of valuable metals in highly contaminated industrial sites depending on economical feasibility.     

Decision making under uncertainty in case of soil remediation

Decisions on soil remediation are one of the most difficult management issues of municipal and state agencies. The assessment of contamination is uncertain, the costs of remediation are high, and the impacts on the environment are multiple. This paper presents a general, transparent, and consistent method for decision making among the remediation alternatives. Soil washing, phytoremediation, and no remediation are exemplarily considered. Multi-criteria utility functions including (a) the cost of remediation (b) the impact on human health and agricultural productivity, and (c) the economic gain after remediation are constructed using probability density functions representing contamination for all site coordinates. Herewith, the probability of different types of (i) correct decisions such as a hit or a true rejection and (ii) erroneous decisions such as a false alarm or miss are examined. The decision theoretic model is applied to a case study on heavy metal contaminated soil. This case study reveals the non-linear structure of multi-criteria-decision making. The case study shows that the geostatistical uncertainties of the log-normal distributed soil contamination must be taken into account: When uncertainties are not considered and the utilities are assessed according to the estimated value for a spatial unit, only few (N=26) spatial units result where the utility score of the alternative soil washing are higher than the utility score to the no remediation alternative. However, when taking into account geostatistical uncertainties of the log-normal soil distribution this number is about ten times greater (N=237). Furthermore, the use of 'maximizing expected utility' as decision rule is critical in that it may lead to a high probability of misses.     

Uncertainty Assessment for Management of Soil Contaminants with Sparse Data

In order for soil resources to be sustainably managed, it is necessary to have reliable, valid data on the spatial distribution of their environmental impact. However, in practice, one often has to cope with spatial interpolation achieved from few data that show a skewed distribution and uncertain information about soil contamination. We present a case study with 76 soil samples taken from a site of 15 square km in order to assess the usability of information gleaned from sparse data. The soil was contaminated with cadmium predominantly as a result of airborne emissions from a metal smelter. The spatial interpolation applies lognormal anisotropic kriging and conditional simulation for log-transformed data. The uncertainty of cadmium concentration acquired through data sampling, sample preparation, analytical measurement, and interpolation is factor 2 within 68.3 % confidence. Uncertainty predominantly results from the spatial interpolation necessitated by low sampling density and spatial heterogeneity. The interpolation data are shown in maps presenting likelihoods of exceeding threshold values as a result of a lognormal probability distribution. Although the results are not deterministic, this procedure yields a quantified and transparent estimation of the contamination, which can be used to delineate areas for soil improvement, remediation, or restricted area use, based on the decision-makers probability safety requirement.     

Effects of initial solute distribution on contaminant availability, desorption modeling, and subsurface remediation

Low permeability regions in which solute movement is governed by diffusion reduce the availability of pollutants for remediation and can function as long-term sources of groundwater contamination. The inherent difficulty in understanding mass transfer from these regions of sequestered contamination is further complicated by unknown solute distributions within the low-permeability regions (sequestering regions). When models are calibrated to reproduce temporal histories of solute release from a sequestering region (desorption), the fitted parameter values are used to infer the physical or chemical characteristics of the media; however, the calibrated parameters also reflect the case-specific initial conditions (i.e., the solute distribution within the sequestering region domain at the onset of desorption). This phenomenon is demonstrated using model simulations of solute diffusion from hypothetical solids with characteristics similar to those of the well studied Borden, Ontario aquifer system. Solute release from the solids is simulated using a batch diffusion model under different initial solute distributions within the solids. The results of these model simulations are used to calibrate parameters of a multiple first-order rate desorption model (MRM) to illustrate how the fitted MRM parameters increase or decrease depending on the initial "aging" of the solids. Further numerical simulations are conducted for a one-dimensional flow system under steady-state and variable-rate hydraulic flushing. These simulations show that although aging reduces desorptive mass flux during early stages of flushing, aged sites have greater desorptive mass flux (greater solute availability) than "freshly" contaminated media during the later stages of remediation. Overall, the results demonstrate why the physicochemical meaning of observed desorption rates cannot be accurately deduced without first understanding the initial solute distribution within the media.     

FUZZY DECISION MAKING IN GROUND WATER NITRATE RISK MANAGEMENT1

ABSTRACT: Ground water nitrate contamination is widespread in the United States and especially prevalent in agriculture-intensive areas such as the Midwest. To reduce human health risks (i.e., methemoglobinemia and cancer risks) from nitrates in ground water supplies, several nitrate risk-management strategies can be developed based on acceptable levels of human health risks, the reasonableness of the cost required for risk reduction, and the technical feasibility of nitrate-control methods. However, due to a lack of available information, assessing risk, cost, and technical feasibility contains elements of uncertainty. In this paper, a nitrate risk-management methodology using fuzzy sets in combination with a multicriterion decision-making (MCDM) technique is developed to assist decision makers in evaluating, with uncertain information, possible regulatory actions along with the various nitrate risk-management strategies in order to determine an appropriate strategy. The methodology is illustrated using data from a community with a nitrate water-quality problem.     

When Environmental Action Does Not Activate Concern: The Case of Impaired Water Quality in Two Rural Watersheds

Little research has considered how residents’ perceptions of their local environment may interact with efforts to increase environmental concern, particularly in areas in need of remediation. This study examined the process by which local environmental action may affect environmental concern. A model was presented for exploring the effects of community-based watershed organizations (CWOs) on environmental concern that also incorporates existing perceptions of the local environment. Survey data were collected from area residents in two watersheds in southwestern Pennsylvania, USA, an area affected by abandoned mine drainage. The findings suggest that residents’ perceptions of local water quality and importance of improving water quality are important predictors of level of environmental concern and desire for action; however, in this case, having an active or inactive CWO did not influence these perceptions. The implications of these findings raise important questions concerning strategies and policy making around environmental remediation at the local level.     

Surface and subsurface geologic risk factors to ground water affecting brownfield redevelopment potential

A model is created for assessing the redevelopment potential of brownfields. The model is derived from a space and time conceptual framework that identifies and measures the surface and subsurface risk factors present at brownfield sites. The model then combines these factors with a contamination extent multiplier at each site to create an index of redevelopment potential. Results from the application of the model within an urbanized watershed demonstrate clear differences between the redevelopment potential present within five different near-surface geologic units, with those units containing clay being less vulnerable to subsurface contamination. With and without the extent multiplier, the total risk present at the brownfield sites within all the geologic units is also strongly correlated to the actual costs of remediation. Thus, computing the total surface and subsurface risk within a watershed can help guide the remediation efforts at broad geographic scales, and prioritize the locations for redevelopment.     

Magnetic particle technology in environmental remediation and wildlife rehabilitation

The use of magnetic particles in environmental remediation and wildlife rehabilitation is currently under investigation at Victoria University, in collaboration with the Phillip Island Research Centre, Victoria, Australia. Iron powder has been shown to be very effective for the magnetic cleansing of feathers and plumage and is almost ideal for this purpose, being non-toxic, a non-irritant and recyclable. Detailed investigations have demonstrated that by varying particle size, particle structure and surface texture, the efficacy of oil removal from feathers and plumage can be successfully manipulated. In this regard, it is possible to identify a grade of iron powder whereby, within experimental error, effectively 100% removal of a variety of fresh contaminants from different matrices, including feathers, can be achieved. Our investigations have been extended to the application of such particles to the successful removal of tarry and weathered/tarry contamination. The results of these investigations have indicated that, for such contaminants, removals ranging from 97–99% may be achieved. Magnetic particle technology may also be adapted for the screening of pre-conditioning agents that can further assist in the removal of tarry and weathered/tarry contamination from feathers. These investigations suggest that magnetic particles could have an important role to play in environmental remediation and wildlife rehabilitation as a clean and effective technology.